{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# sklearn-LDA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "代码示例：https://mp.weixin.qq.com/s/hMcJtB3Lss1NBalXRTGZlQ （玉树芝兰） <br>\n",
    "可视化：https://blog.csdn.net/qq_39496504/article/details/107125284  <br>\n",
    "sklearn lda参数解读:https://scikit-learn.org/stable/modules/generated/sklearn.decomposition.LatentDirichletAllocation.html\n",
    "<br>中文版参数解读：https://blog.csdn.net/TiffanyRabbit/article/details/76445909\n",
    "<br>LDA原理-视频版：https://www.bilibili.com/video/BV1t54y127U8\n",
    "<br>LDA原理-文字版：https://www.jianshu.com/p/5c510694c07e\n",
    "<br>score的计算方法：https://github.com/scikit-learn/scikit-learn/blob/844b4be24d20fc42cc13b957374c718956a0db39/sklearn/decomposition/_lda.py#L729\n",
    "<br>主题困惑度1：https://blog.csdn.net/weixin_43343486/article/details/109255165\n",
    "<br>主题困惑度2：https://blog.csdn.net/weixin_39676021/article/details/112187210"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.预处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "import re\n",
    "import jieba\n",
    "import jieba.posseg as psg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "output_path = 'D:/python/lda/result'\n",
    "file_path = 'D:/python/lda/data'\n",
    "os.chdir(file_path)\n",
    "data=pd.read_excel(\"data.xlsx\")#content type\n",
    "os.chdir(output_path)\n",
    "dic_file = \"D:/python/lda/stop_dic/dict.txt\"\n",
    "stop_file = \"D:/python/lda/stop_dic/stopwords.txt\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\software\\anaconda\\lib\\site-packages\\ipykernel\\ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
      "  and should_run_async(code)\n"
     ]
    }
   ],
   "source": [
    "def chinese_word_cut(mytext):\n",
    "    jieba.load_userdict(dic_file)\n",
    "    jieba.initialize()\n",
    "    try:\n",
    "        stopword_list = open(stop_file,encoding ='utf-8')\n",
    "    except:\n",
    "        stopword_list = []\n",
    "        print(\"error in stop_file\")\n",
    "    stop_list = []\n",
    "    flag_list = ['n','nz','vn']\n",
    "    for line in stopword_list:\n",
    "        line = re.sub(u'\\n|\\\\r', '', line)\n",
    "        stop_list.append(line)\n",
    "    \n",
    "    word_list = []\n",
    "    #jieba分词\n",
    "    seg_list = psg.cut(mytext)\n",
    "    for seg_word in seg_list:\n",
    "        word = re.sub(u'[^\\u4e00-\\u9fa5]','',seg_word.word)\n",
    "        #word = seg_word.word  #如果想要分析英语文本，注释这行代码，启动下行代码\n",
    "        find = 0\n",
    "        for stop_word in stop_list:\n",
    "            if stop_word == word or len(word)<2:     #this word is stopword\n",
    "                    find = 1\n",
    "                    break\n",
    "        if find == 0 and seg_word.flag in flag_list:\n",
    "            word_list.append(word)      \n",
    "    return (\" \").join(word_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\software\\anaconda\\lib\\site-packages\\ipykernel\\ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
      "  and should_run_async(code)\n"
     ]
    }
   ],
   "source": [
    "data[\"content_cutted\"] = data.content.apply(chinese_word_cut)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.LDA分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\software\\anaconda\\lib\\site-packages\\ipykernel\\ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
      "  and should_run_async(code)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer\n",
    "from sklearn.decomposition import LatentDirichletAllocation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\software\\anaconda\\lib\\site-packages\\ipykernel\\ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
      "  and should_run_async(code)\n"
     ]
    }
   ],
   "source": [
    "def print_top_words(model, feature_names, n_top_words):\n",
    "    tword = []\n",
    "    for topic_idx, topic in enumerate(model.components_):\n",
    "        print(\"Topic #%d:\" % topic_idx)\n",
    "        topic_w = \" \".join([feature_names[i] for i in topic.argsort()[:-n_top_words - 1:-1]])\n",
    "        tword.append(topic_w)\n",
    "        print(topic_w)\n",
    "    return tword"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\software\\anaconda\\lib\\site-packages\\ipykernel\\ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
      "  and should_run_async(code)\n"
     ]
    }
   ],
   "source": [
    "n_features = 1000 #提取1000个特征词语\n",
    "tf_vectorizer = CountVectorizer(strip_accents = 'unicode',\n",
    "                                max_features=n_features,\n",
    "                                stop_words='english',\n",
    "                                max_df = 0.5,\n",
    "                                min_df = 10)\n",
    "tf = tf_vectorizer.fit_transform(data.content_cutted)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\software\\anaconda\\lib\\site-packages\\ipykernel\\ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
      "  and should_run_async(code)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LatentDirichletAllocation(learning_offset=50, max_iter=50, n_components=8,\n",
       "                          random_state=0)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n_topics = 8\n",
    "lda = LatentDirichletAllocation(n_components=n_topics, max_iter=50,\n",
    "                                learning_method='batch',\n",
    "                                learning_offset=50,\n",
    "#                                 doc_topic_prior=0.1,\n",
    "#                                 topic_word_prior=0.01,\n",
    "                               random_state=0)\n",
    "lda.fit(tf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.1输出每个主题对应词语 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Topic #0:\n",
      "电影 票房 影片 观众 演员 作品 故事 电影节 娱乐 合作 主演 角色 内地 市场 香港电影 新片 方面 制作 公司 剧本 媒体 人生 开奖 本片 成绩\n",
      "Topic #1:\n",
      "比赛 球队 主场 时间 火箭 球员 联赛 总决赛 篮板 奇才 客场 赛事 机会 新浪 战队 赛区 状态 问题 体育讯 助攻 情况 训练 湖人 内线 球迷\n",
      "Topic #2:\n",
      "主持人 工作 时间 研究 网站 新浪 记者 生活 朋友 现场 感觉 科学家 过程 人们 人类 事情 互联网 孩子 问题 网友 人员 地方 世界 经历 文化\n",
      "Topic #3:\n",
      "项目 建筑 生活 地产 区域 空间 新浪 户型 活动 房子 发展 别墅 设计 产品 国际 文化 主持人 艺术 论坛 市场 客户 住宅 集团 高端 评论\n",
      "Topic #4:\n",
      "学生 主队 赔率 大学 学校 公司 数据 教育 专业 情况 客胜 移民 问题 留学生 语言 能力 主场 客队 家长 足彩 建议 成绩 课程 论文 英语\n",
      "Topic #5:\n",
      "经济 投资 市场 公司 发展 政府 企业 问题 政策 计划 国家 银行 影响 资金 基金 服务 管理 业务 行业 产业 文章 商业 机构 风险 全球\n",
      "Topic #6:\n",
      "游戏 电子竞技 玩家 手机 奖金 公司 网络 世界 用户 职业 发展 冠军 平台 任务 俱乐部 全球 行业 系统 模式 国际 活动 太空 爱好者 项目 星际\n",
      "Topic #7:\n",
      "专家 网友 老师 压力 走势 分析 突破 黄金 问题 股票 大盘 建议 新浪 整理 成本 调整 a股 机会 坐堂 趋势 股市 后市 行情 市场 价位\n"
     ]
    }
   ],
   "source": [
    "n_top_words = 25\n",
    "tf_feature_names = tf_vectorizer.get_feature_names()\n",
    "topic_word = print_top_words(lda, tf_feature_names, n_top_words)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2输出每篇文章对应主题 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "topics=lda.transform(tf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "topic = []\n",
    "for t in topics:\n",
    "    topic.append(\"Topic #\"+str(list(t).index(np.max(t))))\n",
    "data['概率最大的主题序号']=topic\n",
    "data['每个主题对应概率']=list(topics)\n",
    "data.to_excel(\"data_topic.xlsx\",index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.3可视化 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pyLDAvis\n",
    "import pyLDAvis.sklearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\software\\anaconda\\lib\\site-packages\\ipykernel\\ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
      "  and should_run_async(code)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "<link rel=\"stylesheet\" type=\"text/css\" href=\"https://cdn.jsdelivr.net/gh/bmabey/pyLDAvis@3.3.1/pyLDAvis/js/ldavis.v1.0.0.css\">\n",
       "\n",
       "\n",
       "<div id=\"ldavis_el524025922287972327948571447\"></div>\n",
       "<script type=\"text/javascript\">\n",
       "\n",
       "var ldavis_el524025922287972327948571447_data = {\"mdsDat\": {\"x\": [-0.013863149987170158, -0.13326757177131823, 0.2571094974137888, 0.11644502456798038, -0.06464774087273016, 0.12715096593502334, -0.07828340707599318, -0.21064361820958075], \"y\": [0.11041221033496819, 0.10801054654675804, 0.21374362350867795, -0.28333293019871114, -0.014086167222612958, -0.06642775551873542, -0.09740993352175875, 0.029090406071413954], \"topics\": [1, 2, 3, 4, 5, 6, 7, 8], \"cluster\": [1, 1, 1, 1, 1, 1, 1, 1], \"Freq\": [17.41423709420961, 14.511345095316555, 14.478576615458227, 14.122054434500091, 12.24621178701099, 11.227999556631419, 9.416113891971781, 6.583461524901326]}, \"tinfo\": {\"Term\": [\"\\u4e13\\u5bb6\", \"\\u6bd4\\u8d5b\", \"\\u7535\\u5f71\", \"\\u7f51\\u53cb\", \"\\u6e38\\u620f\", \"\\u8001\\u5e08\", \"\\u5b66\\u751f\", \"\\u4e3b\\u961f\", \"\\u8d54\\u7387\", \"\\u4e3b\\u6301\\u4eba\", \"\\u7403\\u961f\", \"\\u538b\\u529b\", \"\\u9879\\u76ee\", \"\\u7ecf\\u6d4e\", 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       "\n",
       "function LDAvis_load_lib(url, callback){\n",
       "  var s = document.createElement('script');\n",
       "  s.src = url;\n",
       "  s.async = true;\n",
       "  s.onreadystatechange = s.onload = callback;\n",
       "  s.onerror = function(){console.warn(\"failed to load library \" + url);};\n",
       "  document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "}\n",
       "\n",
       "if(typeof(LDAvis) !== \"undefined\"){\n",
       "   // already loaded: just create the visualization\n",
       "   !function(LDAvis){\n",
       "       new LDAvis(\"#\" + \"ldavis_el524025922287972327948571447\", ldavis_el524025922287972327948571447_data);\n",
       "   }(LDAvis);\n",
       "}else if(typeof define === \"function\" && define.amd){\n",
       "   // require.js is available: use it to load d3/LDAvis\n",
       "   require.config({paths: {d3: \"https://d3js.org/d3.v5\"}});\n",
       "   require([\"d3\"], function(d3){\n",
       "      window.d3 = d3;\n",
       "      LDAvis_load_lib(\"https://cdn.jsdelivr.net/gh/bmabey/pyLDAvis@3.3.1/pyLDAvis/js/ldavis.v3.0.0.js\", function(){\n",
       "        new LDAvis(\"#\" + \"ldavis_el524025922287972327948571447\", ldavis_el524025922287972327948571447_data);\n",
       "      });\n",
       "    });\n",
       "}else{\n",
       "    // require.js not available: dynamically load d3 & LDAvis\n",
       "    LDAvis_load_lib(\"https://d3js.org/d3.v5.js\", function(){\n",
       "         LDAvis_load_lib(\"https://cdn.jsdelivr.net/gh/bmabey/pyLDAvis@3.3.1/pyLDAvis/js/ldavis.v3.0.0.js\", function(){\n",
       "                 new LDAvis(\"#\" + \"ldavis_el524025922287972327948571447\", ldavis_el524025922287972327948571447_data);\n",
       "            })\n",
       "         });\n",
       "}\n",
       "</script>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pyLDAvis.enable_notebook()\n",
    "pic = pyLDAvis.sklearn.prepare(lda, tf, tf_vectorizer)\n",
    "pyLDAvis.display(pic)\n",
    "pyLDAvis.save_html(pic, 'lda_pass'+str(n_topics)+'.html')\n",
    "pyLDAvis.display(pic)\n",
    "#去工作路径下找保存好的html文件\n",
    "#和视频里讲的不一样，目前这个代码不需要手动中断运行，可以快速出结果"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.4困惑度 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\software\\anaconda\\lib\\site-packages\\ipykernel\\ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
      "  and should_run_async(code)\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\software\\anaconda\\lib\\site-packages\\ipykernel\\ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
      "  and should_run_async(code)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "13\n",
      "14\n",
      "15\n"
     ]
    }
   ],
   "source": [
    "plexs = []\n",
    "scores = []\n",
    "n_max_topics = 16\n",
    "for i in range(1,n_max_topics):\n",
    "    print(i)\n",
    "    lda = LatentDirichletAllocation(n_components=i, max_iter=50,\n",
    "                                    learning_method='batch',\n",
    "                                    learning_offset=50,random_state=0)\n",
    "    lda.fit(tf)\n",
    "    plexs.append(lda.perplexity(tf))\n",
    "    scores.append(lda.score(tf))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\software\\anaconda\\lib\\site-packages\\ipykernel\\ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
      "  and should_run_async(code)\n"
     ]
    },
    {
     "data": {
      "image/png": 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MtGQevnoEHdqmcd2kOWwo2xt2JBFpIWK9HWdnM/uTmU0NXg8ws+sb2GwfcK67nwgMBUab2anB1+5196HBY0rdZwITiEzRPRq4PzjILUchPyudR689mcr9NVz/2Bx2VlaFHUlEWoBYT0l9DHgZ6Bq8Xk4D91PwiLqJ/1ODx+HGqsYDT7r7PndfDawkcgtQOUrHd87igSuHs7JkFzc9Pp+qmtqwI4lIMxdrKXRy96eBWgB3rwYanNDfzJLNbAGRq5+nufu7wZduNrOFZvZnM2sfrOsGrKu3+fpg3YGfeaOZzTWzuaWlpTHGT1yn9+3Ef18yiDeXl/KT54uJ5RiSiCSuWEtht5l1JPhNPxgGOvCezZ/i7jXuPhQoBEaa2SDgAaAPkSGlTUTmVYLIRHuf+oiDfOZDwY1+RuTl5cUYP7FNGNmDb57dh8ffXctDb64KO46INGOxnpL6HeB5oLeZvQ3kAZfF+k3cvczMXgdGu3v0Hg1m9jAwOXi5Huheb7NCYGOs30MO7z8+ewJrt+/hl1OX0b1DJmMGF4QdSUSaoVj3FJYA/wTmEJnu4mEixxUOyczyzCw3WM4AzgeWmVn9n0aXAouD5eeBCWbWxsx6AX2B2THmkwYkJRm//uKJnNQjl9ufWsD8tTvCjiQizVCspfAXIjfY+QUwkcgP7L82sE0B8JqZLSRSJtPcfTLwP2a2KFh/DnA7gLsXA08TKaCXgJuCq6mlkaSnJvPwV0fQOTudr02ay7rte8KOJCLNTKwXr70fnFp62HVNTRevHZ0PS3fx+ftn0qldGs9+cxQ5malhRxKRJnTMF68B8+tdY4CZnQK83RjhpOn1yWvHH68aztrte/jG3+axv1qnqopIRKylcAqR+Y/WmNkaYBZwVr1hIGlhTu3dkV99YQizVm3jh/9cpFNVRQSI/eyj0XFNIaH4/EmFfLRtD797dQVFHTO5+dy+YUcSkZDFVAru/lG8g0g4bju/L2u37+GeV5bTvUMm44d+6npBEUkgse4pSCtlZtz9hcFsKNvLf/zfQrrmZnByUYewY4lISGI9piCtWJuUZB66ajiF7TO44S9zWb5lZ9iRRCQkKgUBIDczjT9fczLJZlw08S3um75CZyWJJCCVgkQVdWrL1NvO4IL+nbnnleVcPPEtXfkskmBUCvIJ+Vnp/OGKk3jkqyOoqKzi8w/M5M7ni9m1rzrsaCLSBFQKclDnD+jMK7efyVdP7cmkWWv47G/e4NWlW8KOJSJxplKQQ8pKT+Wn4wfxzDc+Q7v0FK6fNJebH3+P0p37wo4mInGiUpAGDe/Znsm3nMF3LzieV4q3cP5v3uDpOet0FbRIK6RSkJikpSRxy3l9mXrbGZzQJYvv/WMhlz/8Lqu37g47mog0IpWCHJE+ee148oZT+cWlg1m8sZzP/fZN/vDaSt3/WaSVUCnIEUtKMi4/pQevfucszuuXz/++/AEXT3yLBevKwo4mIsdIpSBHLT87nQeuHM5DVw2nbE8Vl97/Nj99oZjdOn1VpMVSKcgx++zALkz7zplceUpPHpu5hs/e+yavLSsJO5aIHAWVgjSKrPRU7rpkEM984zQy05K59rE5On1VpAVSKUijGt6zA5O/fTq3n6/TV0VaIpWCNLo2Kcncen5fptx6Osd3bqfTV0VaEJWCxM1x+Vk8deNp/PzSQSzeUM7o377Jg298SG2t9hpEmiuVgsRVUpJxxSk9+fd3z+LsE/K4e+oybvzrXCoqq8KOJiIHoVKQJtE5O50HrxzOnRcP4PUPShl/39t8sFk38xFpblQK0mTMjGtG9eKJG09l175qLr3/bSYv3Bh2LBGpR6UgTe7kog5MvuV0+hdkc/Pj8/nFlKVUa5oMkWZBpSCh6JydzhM3nMpVp/bkoTdXcdWfZrN1l65pEAmbSkFCk5aSxF2XDOKeL57Ie2t3aP4kkWZApSChu2x4If/45mdIMuNLD87iydlrw44kkrBUCtIsDOqWw+RbTueU3h34wbOLuOPZheyrrgk7lkjCUSlIs9G+bRqPXTuSb53dhydmr+NLf3yHjWV7w44lklBUCtKsJCcZ3xvdjwevHM6HJbu4eOJbzPxwa9ixRBKGSkGapdGDuvCvm0aRm5nKVX+azcNvrtKkeiJNQKUgzdZx+e147ubTuaB/Z34+ZSk3PzFfN/ARiTOVgjRr7dqk8MCVJ/H90f2YumgTl97/tmZbFYkjlYI0e2bGN8/uw6TrRlK6cx/jJr7Fv5dsCTuWSKukUpAW44y+ebxwy+n07JTJ1/4yl99MW65puEUamUpBWpTC9pk8843PcNnwQn7/6gqunzSH8j2ahlukscStFMws3cxmm9n7ZlZsZj8N1ncws2lmtiJ4bl9vmzvMbKWZfWBmn4tXNmnZ0lOT+d/LhvDflwzirZVbufi+t1i6qSLsWCKtQjz3FPYB57r7icBQYLSZnQr8AHjV3fsCrwavMbMBwARgIDAauN/MkuOYT1owM+PKU3vy5I2nsa+6hkvvf5vnFmwIO5ZIixe3UvCIXcHL1ODhwHhgUrB+EnBJsDweeNLd97n7amAlMDJe+aR1GN6zPS/ccjpDuuVy65MLuObR2cz7aHvYsURarLgeUzCzZDNbAJQA09z9XaCzu28CCJ7zg7d3A9bV23x9sO7Az7zRzOaa2dzS0tJ4xpcWIj8rnb/fcArfH92PhevL+cIDs/jKQ+8w88OtuuBN5AjFtRTcvcbdhwKFwEgzG3SYt9vBPuIgn/mQu49w9xF5eXmNlFRautTkJL55dh/e+v45/OfY/nxYuovLH36Xyx6cxWsflKgcRGLUJGcfuXsZ8DqRYwVbzKwAIHguCd62Huheb7NCQPdqlCOSmZbC187ozZvfO4e7xg9kU9lern10DuPue5uXizfrFFaRBsTz7KM8M8sNljOA84FlwPPA1cHbrgaeC5afByaYWRsz6wX0BWbHK5+0bumpyVx1WhGv/8c5/OoLg6morOLrf53HmN/P4IX3N1KjchA5KIvXbrWZDSFyIDmZSPk87e4/M7OOwNNAD2At8EV33x5s8yPgOqAauM3dpx7ue4wYMcLnzp0bl/zSulTX1DJ54Sbue20lK0t20TuvLTedfRzjhnYlNVmX60hiMbN57j7ioF9ryWOtKgU5UrW1zkvFm5k4fSVLN1XQvUMG3zzrOL4wvBttUnQGtCQGlYLIAdydV5eWMHH6Ct5fX05BTjpfP7M3E0b2ID1V5SCtm0pB5BDcnRkrtjJx+grmrNlBp3ZtuPHMXlxxSk/atkkJO55IXKgURGLw7qptTJy+krdWbiU3M5XrR/Xi6lFFZKenhh1NpFGpFESOwHtrd3Df9JVMX1ZCVnoK13ymiOtG9aJ927Swo4k0CpWCyFFYvKGc+6av5KXizWSmJXPFKT24dlQvuuZmhB1N5JioFESOwfItO/nDayuZvHATBlw0pICvndGbQd1ywo4mclRUCiKNYN32PTz69hqemrOW3ftrGHVcR244ozdnHZ+H2cFmaRFpnlQKIo2ofG8VT8xey6Nvr2ZLxT5O6JzF187oxbihXXWtg7QIKgWRONhfXcsL72/k4RmrWLZ5J/lZbbhmVBFXjOxJTqbOWJLmS6UgEkd11zo8PGMVM1ZsJTMtmS+N6M71p/eie4fMsOOJfIpKQaSJLNlYwSNvreL5BRupdefCwQXceEZvTuye26Q5qmtqWVm6i+INFSzdVEHX3AwuHNyFghydOSUqBZEmt6l8L4/NXMPj76xl575qRvbqwI1n9ObcfvkkJTXuQenKqhqWbd5J8cZyFm+oYMnGcpZu3sn+6loA0pKT2F8TWT6pRy5jBhcwZnCBTq1NYCoFkZDsrKziqTnrePTtNWwo20vvvLbccEZvLh3W7ajmWKqorGLJxgqKN1ZQvLGc4g0VrCzdFZ0KPDs9hYFdcxjULTv63KtTOz7atpspizbx4qLNLN1UAcCwHrmMHVzA6EFdKGyvYa5EolIQCVlVTS1TFm3i4RmrWLyhgo5t0/jqaUVcdVpPOhziSumtu/ZRvLGCxRvKWbKxgsUby/lo257o1/Oz2jCwazaDuuUwsGukBArbZzR4euzqrZGCmLJoE8UbIwVxYvdcxg7uwoWDCnQcJAGoFESaCXfnnVXbeXjGKqYvKyE9NYnLhhfyxeHd2VxRSfGG8mAvoILNFZXR7Xp0yIwWwICu2Qzsmk1+Vvox51mzdTdTF29myqJNLNpQDsCQwpzIENOgAnp0VEG0RioFkWZoxZadPDJjNf+cvyE65p9kcFx+OwZ2/fi3/wFds8nJiP8prmu37WHK4sgexML1kYIY1C2bMYMLGDu4gJ4d28Y9gzQNlYJIM1ays5KZK7fRs2Mm/bpkk5EW/gVw67bvYeriyDGI99eVATCgIJuxQyIHqXt1UkG0ZCoFETlq63fs4aXFm3lx0Sbmry0DoH9BNmMGdWHMkAL65LULN6AcMZWCiDSKDWV7mRocpH4vKIgTOmdxbv98zuuXz7Ae7Ulu5FNupfGpFESk0W0q38vURZt5uXgzcz/aQU2tk5uZytnH53Fu/86c1TdP0300UyoFEYmr8r1VzFhRyvSlJby+vJTtu/eTnGQM79mec/tF9iKOy2+n2WSbCZWCiDSZmlpnwboyXltWwqvLSqIXyxW2z+C8fvmc0y+fU3t3PKqL96RxqBREJDQby/by2gclvLashLdWbqWyqpaM1GRGHdeJ8/rnc84J+XTJOfZrLiR2KgURaRYqq2qYtWob05eWMH1ZCRvK9gIwsGt2dC/ixMLcRp8fSj5JpSAizY67s3zLLqYvK2H6si3M+2gHtQ6d2qVx1vH5nNc/nzP6diIrXQerG5tKQUSavR279/PmilJeXVrCG8tLKd9bRXKS0aldGrkZaeRkppKbkUpuZiq5mWmR54y659TI1zPTyM1IJTMtOW4HtfdV17B7Xw27KqvZua+KXZXV7NoXeeysrGZ3sFxZVUNeVhu65mZEHjkZ5GW1aRan7B6uFFKaOoyIyMG0b5vG+KHdGD+0G9U1tby3toy3VpSypWIfO/bsp2xvFR9t28P76/ezY09VdGrwg0lNNnLqFUZuvcLIzUwlJ1hOSbLoD/S6H+47g+Xd9Zbrv6duSpLDMYtMWb7vgIwpSUaXnHS65mbQLTeDrrnpFOTULUdeh71npFIQkWYnJTmJkb06MLJXh0O+p7KqhrI9VZTt3R953lNFed3y3qpgXeT1hrJKlmysoGxvFXv21xzyM9ukJJGVnkK7Nim0S0+hbVoKXXPTo6/btUmlXZvk4HUq7dqkRN/ftt5y3Z5KRWUVG8v2srFsLxvKKqPLm8oqmb16O5srKqPTntfJSk+JlkTBJwokUhqds9NJTU5qtP/WB1IpiEiLlJ6aTJec5CM+c2lfdQ3le6so31NFVY1/4od6Wkrj/rDNTk8lu0sq/bpkH/TrNbVOyc7KaGlsOqBA3lu7g7I9VZ/YJsmgc3Y6YwcX8J8XDWjUvKBSEJEE0yYlmfys5EaZevxYJScZBTkZFORkMLznwd+zZ381G+vtZdSVRrxO41UpiIg0Y5lpKRyX347j8ptm4sH4DUyJiEiLo1IQEZEolYKIiESpFEREJEqlICIiUSoFERGJUimIiEiUSkFERKJa9CypZlYKfBR2jkPoBGwNO8RRUvam11Jzg7KH5Viy93T3vIN9oUWXQnNmZnMPNTVtc6fsTa+l5gZlD0u8smv4SEREolQKIiISpVKIn4fCDnAMlL3ptdTcoOxhiUt2HVMQEZEo7SmIiEiUSkFERKJUCo3MzLqb2WtmttTMis3s1rAzHQkzSzaz+WY2OewsR8LMcs3sGTNbFvy3Py3sTLEys9uDvyuLzewJMwv/lmCHYGZ/NrMSM1tcb10HM5tmZiuC5/ZhZjyUQ2T/3+DvzEIz+6eZ5YYY8ZAOlr3e1/6fmbmZdWqM76VSaHzVwHfdvT9wKnCTmTX+jVTj51ZgadghjsLvgJfcvR9wIi3kz2Bm3YBvAyPcfRCQDEwIN9VhPQaMPmDdD4BX3b0v8Grwujl6jE9nnwYMcvchwHLgjqYOFaPH+HR2zKw7cAGwtrG+kUqhkbn7Jnd/L1jeSeSHU7dwU8XGzAqBscAjYWc5EmaWDZwJ/AnA3fe7e1mooY5MCpBhZilAJrAx5DyH5O5vAtsPWD0emBQsTwIuacpMsTpYdnd/xd2rg5fvAIVNHiwGh/jvDnAv8D2g0c4YUinEkZkVAcOAd0OOEqvfEvkLVhtyjiPVGygFHg2Gvh4xs7Zhh4qFu28A7iHym94moNzdXwk31RHr7O6bIPJLEZAfcp6jdR0wNewQsTKzccAGd3+/MT9XpRAnZtYO+Adwm7tXhJ2nIWZ2EVDi7vPCznIUUoCTgAfcfRiwm+Y7hPEJwfj7eKAX0BVoa2ZXhpsq8ZjZj4gM/f497CyxMLNM4EfAjxv7s1UKcWBmqUQK4e/u/mzYeWI0ChhnZmuAJ4Fzzexv4UaK2XpgvbvX7ZE9Q6QkWoLzgdXuXuruVcCzwGdCznSktphZAUDwXBJyniNiZlcDFwFXeMu5cKsPkV8k3g/+zRYC75lZl2P9YJVCIzMzIzK2vdTdfxN2nli5+x3uXujuRUQOdE539xbxG6u7bwbWmdkJwarzgCUhRjoSa4FTzSwz+LtzHi3kIHk9zwNXB8tXA8+FmOWImNlo4PvAOHffE3aeWLn7InfPd/ei4N/seuCk4N/CMVEpNL5RwFVEftNeEDzGhB0qAdwC/N3MFgJDgV+EGyc2wd7NM8B7wCIi/yab7dQLZvYEMAs4wczWm9n1wN3ABWa2gsiZMHeHmfFQDpH9PiALmBb8W30w1JCHcIjs8fleLWdvSURE4k17CiIiEqVSEBGRKJWCiIhEqRRERCRKpSAiIlEqBZGAmb1uZnG/ibuZfTuYyfXvB6wfeiynL5tZVzN75tgTSiJLCTuASGtgZin1JlZryLeAC9199QHrhwIjgClHk8HdNwKXHc22InW0pyAtipkVBb9lPxzcg+AVM8sIvhb9Td/MOgWX/2Nm15jZv8zsBTNbbWY3m9l3gsnz3jGzDvW+xZVmNjO4t8HIYPu2wXz2c4Jtxtf73P8zsxeAT01iF3yPxcHjtmDdg0Qm8HvezG6v99404GfAl4OLqL4c3KfgX8Fc/++Y2ZDgvXea2V/NbHpwD4Mb6v23WRwsJ5vZPWa2KNj+lmD93Wa2JFh3T+P9n5HWQnsK0hL1Bb7i7jeY2dPAF4CG5mkaRGTG2nRgJfB9dx9mZvcCXyUyQyxAW3f/jJmdCfw52O5HRKb9uM4iN2GZbWb/Dt5/GjDE3T8xrbGZDQeuBU4BDHjXzN5w928EUyuc4+5b697v7vvN7MdE7qtwc/AZE4H57n6JmZ0L/IXI3gTAECL362gLzDezFw/4895IZG6cYe5eHRRMB+BSoJ+7uzXTG8pIuLSnIC3RandfECzPA4pi2OY1d9/p7qVAOfBCsH7RAds/AdH567ODH5yfBX5gZguA14kUS4/g/dMOLITA6cA/3X23u+8iMtHdGTHkPPAz/hrkmQ50NLOc4GvPufveoFheA0YesO35wIN1Q1pBxgqgEnjEzD4PtJi5fqTpqBSkJdpXb7mGj/d4q/n47/SBt7Ssv01tvde1fHKP+cB5X5zIb/pfcPehwaOHu9dNWrf7EBnt8H+EmBzsM/yA5wPX19/2E+uCghhJZAbfS4CXjj2itDYqBWlN1gDDg+WjPeD6ZQAzO53IDW/KgZeBW4JZTDGzYTF8zpvAJcHsp22JDNvMaGCbnUQmZ6v/GVcE3/NsYGu9e3OMN7N0M+sInA3MOeCzXgG+YZG7udXdR7kdkOPuU4Db+HgoSiRKxxSkNbkHeNrMrgKmH+Vn7DCzmUA2kTtxAdxF5JjDwqAY1hCZf/+Q3P09M3sMmB2sesTd5zfwvV/j42GqXwJ3Ermb3EIiQz1X13vvbOBFIsNYd7n7Rovc6a/OI8DxQeYq4GEiewjPmVk6kT2J2xE5gGZJFWlhzOxOYJe76+whaXQaPhIRkSjtKYiISJT2FEREJEqlICIiUSoFERGJUimIiEiUSkFERKL+P/KCHGX11BVfAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "n_t=15#区间最右侧的值。注意：不能大于n_max_topics\n",
    "x=list(range(1,n_t+1))\n",
    "plt.plot(x,plexs[0:n_t])\n",
    "plt.xlabel(\"number of topics\")\n",
    "plt.ylabel(\"perplexity\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {
    "height": "303.324px",
    "left": "114px",
    "top": "110.322px",
    "width": "165px"
   },
   "toc_section_display": true,
   "toc_window_display": true
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
